Neuro-Fuzzy Model for Quantified Rainfall Prediction Using Data Mining and Soft Computing Approaches

ABSTRACT

In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the “If-then” rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data.

Keywords:

• Neuro-Fuzzy model
• Association rules
• Cluster membership
• Dense datasets
• Rainfall
• Prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

H. Vathsala

Vathsala H has a PhD degree from NITK. She is employed in Center for Development of Advanced Computing, Bangalore. She has around 11 years of experience in software development. She is currently working on weather forecasting and data mining techniques. Her area of interest includes data mining, machine learning and high performance computing.

Shashidhar G. Koolagudi

Shashidhar G Koolagudi has a PhD from IIT Kharagpur, he has worked on using different machine learning algorithms for processing big data applications like speech processing during his PhD. Currently, he is working as associate professor in NITK, Surathkal. He has an experience of around 15 years in the fields of teaching and research. His area of interest include machine learning, signal processing and big data analytics. Email: [email protected]